School truancy is a significant problem that affects the educational environment and student achievement. This article presents a project to develop an automated absence detection system for classrooms using Haar Cascade and Local Binary Patterns Histogram (LBHP) techniques. The study begins by collecting a large dataset of classroom images, including various lighting scenarios and conditions. Haar Cascade is used to detect human faces in images, followed by LBHP feature extraction for each detected face. Experimental results demonstrate the effectiveness of the proposed system, achieving a high accuracy rate. This project contributes to the field of educational technology by providing a practical solution for monitoring classroom attendance. The integration of Haar Cascade and LBHP techniques provides robust and efficient performance in absence detection.